Centralized and Federated Models for the Analysis of Clinical Data

Area of Science:

• Clinical data analysis
• Precision medicine research

Background:

• Precision medicine relies on analyzing large, diverse clinical datasets.
• Increasing data complexity necessitates effective aggregation methods for comprehensive disease understanding.

Purpose of the Study:

• To review and compare centralized and federated models for diverse clinical data analysis.
• To discuss methodologies, challenges, and future opportunities for these data analysis approaches.

Main Methods:

• Comparative analysis of centralized and federated data models.
• Review of current methodologies and associated challenges in clinical data aggregation.

Main Results:

• Centralized models offer simplicity but raise privacy concerns.
• Federated models enhance privacy but introduce complexity in analysis.
• Both models present distinct advantages and disadvantages for clinical data integration.

Conclusions:

• The choice between centralized and federated models depends on specific research needs and constraints.
• Advancements in both approaches are crucial for the future of precision medicine and clinical data analysis.